From d34abf63c8c2ce5a3e748aff10e106f5934fb2f5 Mon Sep 17 00:00:00 2001
From: Chakib Benziane <chakib.benz@gmail.com>
Date: Sat, 1 Apr 2017 00:26:12 +0000
Subject: [PATCH] fix get batches

---
 .../dlnd_tv_script_generation.ipynb           | 571 ++++++++++--------
 ...ts.out.tfevents.1490999151.ip-172-31-18-64 | Bin 582955 -> 584497 bytes
 tv-script-generation/params.p                 |   2 +-
 tv-script-generation/preprocess.p             |   4 +-
 tv-script-generation/save.data-00000-of-00001 |   4 +-
 tv-script-generation/save.index               |   4 +-
 tv-script-generation/save.meta                |   4 +-
 7 files changed, 339 insertions(+), 250 deletions(-)

diff --git a/tv-script-generation/dlnd_tv_script_generation.ipynb b/tv-script-generation/dlnd_tv_script_generation.ipynb
index 2a55cc9..8840d5a 100644
--- a/tv-script-generation/dlnd_tv_script_generation.ipynb
+++ b/tv-script-generation/dlnd_tv_script_generation.ipynb
@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -106,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
@@ -143,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -156,7 +156,7 @@
        "\n",
        "    <div class=\"bk-root\">\n",
        "        <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
-       "        <span id=\"5d944cec-21f7-43a6-9d66-027ae50d0773\">Loading BokehJS ...</span>\n",
+       "        <span id=\"a1d47867-3c6a-4b12-b791-9b6e6f68b735\">Loading BokehJS ...</span>\n",
        "    </div>"
       ]
      },
@@ -204,7 +204,7 @@
        "\n",
        "  function display_loaded() {\n",
        "    if (window.Bokeh !== undefined) {\n",
-       "      document.getElementById(\"5d944cec-21f7-43a6-9d66-027ae50d0773\").textContent = \"BokehJS successfully loaded.\";\n",
+       "      document.getElementById(\"a1d47867-3c6a-4b12-b791-9b6e6f68b735\").textContent = \"BokehJS successfully loaded.\";\n",
        "    } else if (Date.now() < window._bokeh_timeout) {\n",
        "      setTimeout(display_loaded, 100)\n",
        "    }\n",
@@ -246,9 +246,9 @@
        "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
        "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
        "    }\n",
-       "  };var element = document.getElementById(\"5d944cec-21f7-43a6-9d66-027ae50d0773\");\n",
+       "  };var element = document.getElementById(\"a1d47867-3c6a-4b12-b791-9b6e6f68b735\");\n",
        "  if (element == null) {\n",
-       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '5d944cec-21f7-43a6-9d66-027ae50d0773' but no matching script tag was found. \")\n",
+       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'a1d47867-3c6a-4b12-b791-9b6e6f68b735' but no matching script tag was found. \")\n",
        "    return false;\n",
        "  }\n",
        "\n",
@@ -261,7 +261,7 @@
        "    \n",
        "    function(Bokeh) {\n",
        "      \n",
-       "      document.getElementById(\"5d944cec-21f7-43a6-9d66-027ae50d0773\").textContent = \"BokehJS is loading...\";\n",
+       "      document.getElementById(\"a1d47867-3c6a-4b12-b791-9b6e6f68b735\").textContent = \"BokehJS is loading...\";\n",
        "    },\n",
        "    function(Bokeh) {\n",
        "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.css\");\n",
@@ -284,7 +284,7 @@
        "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
        "      window._bokeh_failed_load = true;\n",
        "    } else if (force !== true) {\n",
-       "      var cell = $(document.getElementById(\"5d944cec-21f7-43a6-9d66-027ae50d0773\")).parents('.cell').data().cell;\n",
+       "      var cell = $(document.getElementById(\"a1d47867-3c6a-4b12-b791-9b6e6f68b735\")).parents('.cell').data().cell;\n",
        "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
        "    }\n",
        "\n",
@@ -311,7 +311,7 @@
        "\n",
        "\n",
        "    <div class=\"bk-root\">\n",
-       "        <div class=\"bk-plotdiv\" id=\"46433a97-e836-4db8-9dbd-6723adbab9ef\"></div>\n",
+       "        <div class=\"bk-plotdiv\" id=\"e6d57401-77d4-4f94-812f-47bd9fad17a0\"></div>\n",
        "    </div>\n",
        "<script type=\"text/javascript\">\n",
        "  \n",
@@ -352,7 +352,7 @@
        "  \n",
        "    function display_loaded() {\n",
        "      if (window.Bokeh !== undefined) {\n",
-       "        document.getElementById(\"46433a97-e836-4db8-9dbd-6723adbab9ef\").textContent = \"BokehJS successfully loaded.\";\n",
+       "        document.getElementById(\"e6d57401-77d4-4f94-812f-47bd9fad17a0\").textContent = \"BokehJS successfully loaded.\";\n",
        "      } else if (Date.now() < window._bokeh_timeout) {\n",
        "        setTimeout(display_loaded, 100)\n",
        "      }\n",
@@ -394,9 +394,9 @@
        "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
        "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
        "      }\n",
-       "    };var element = document.getElementById(\"46433a97-e836-4db8-9dbd-6723adbab9ef\");\n",
+       "    };var element = document.getElementById(\"e6d57401-77d4-4f94-812f-47bd9fad17a0\");\n",
        "    if (element == null) {\n",
-       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '46433a97-e836-4db8-9dbd-6723adbab9ef' but no matching script tag was found. \")\n",
+       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'e6d57401-77d4-4f94-812f-47bd9fad17a0' but no matching script tag was found. \")\n",
        "      return false;\n",
        "    }\n",
        "  \n",
@@ -406,8 +406,8 @@
        "      function(Bokeh) {\n",
        "        (function() {\n",
        "          var fn = function() {\n",
-       "            var docs_json = {\"b315479b-aa1c-4b1d-a62f-5f3345d28e0e\":{\"roots\":{\"references\":[{\"attributes\":{\"callback\":null,\"column_names\":[\"label\",\"line_alpha\",\"fill_alpha\",\"width\",\"height\",\"line_color\",\"color\",\"x\",\"y\"],\"data\":{\"chart_index\":[\"(114.640000, 117.200000]\"],\"color\":[\"#f22c40\"],\"fill_alpha\":[0.8],\"height\":[1.0],\"label\":[\"(114.640000, 117.200000]\"],\"line_alpha\":[1.0],\"line_color\":[\"black\"],\"width\":[2.5600000000000023],\"x\":[\"115.92\"],\"y\":[0.5]}},\"id\":\"79a32e08-93fb-4949-910c-bddca8c1a9c8\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"fbc40265-c78f-4a25-ae94-6d3a7c9f0546\",\"type\":\"Rect\"},{\"attributes\":{\"dimension\":1,\"plot\":{\"id\":\"197186d2-75e3-4931-8078-3a33f3d5ec66\",\"subtype\":\"Chart\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"59b00378-9769-49dd-93a5-5ea78952574a\",\"type\":\"BasicTicker\"}},\"id\":\"41110901-9156-44a9-a2df-29b66053bb54\",\"type\":\"Grid\"},{\"attributes\":{\"data_source\":{\"id\":\"51803eef-a774-4346-84f2-207e88a53148\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"0656517c-e3ae-4649-944f-a7010c347a13\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"1bc832f0-d593-4694-bd56-66053fdb0303\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"axis_label\":\"sentence 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19.920000]\"],\"color\":[\"#f22c40\"],\"fill_alpha\":[0.8],\"height\":[233.0],\"label\":[\"(17.360000, 19.920000]\"],\"line_alpha\":[1.0],\"line_color\":[\"black\"],\"width\":[2.5600000000000023],\"x\":[\"18.64\"],\"y\":[116.5]}},\"id\":\"725fd89c-ef0c-46f7-92af-243cd15b8ae4\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"data_source\":{\"id\":\"624a4deb-817f-4609-a054-4a04898eafa8\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"173d5e41-b2c7-4391-876b-2fff132c03d9\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"a4329407-f831-4d25-85d1-66960fe7d31e\",\"type\":\"GlyphRenderer\"}],\"root_ids\":[\"72dc7dee-c815-47a1-ac56-5227256aa304\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.4\"}};\n",
+       "            var render_items = [{\"docid\":\"9f84cecc-8ae3-424e-8008-c4f2108b2e40\",\"elementid\":\"e6d57401-77d4-4f94-812f-47bd9fad17a0\",\"modelid\":\"72dc7dee-c815-47a1-ac56-5227256aa304\"}];\n",
        "            \n",
        "            Bokeh.embed.embed_items(docs_json, render_items);\n",
        "          };\n",
@@ -432,7 +432,7 @@
        "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
        "        window._bokeh_failed_load = true;\n",
        "      } else if (force !== true) {\n",
-       "        var cell = $(document.getElementById(\"46433a97-e836-4db8-9dbd-6723adbab9ef\")).parents('.cell').data().cell;\n",
+       "        var cell = $(document.getElementById(\"e6d57401-77d4-4f94-812f-47bd9fad17a0\")).parents('.cell').data().cell;\n",
        "        cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
        "      }\n",
        "  \n",
@@ -510,7 +510,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -576,7 +576,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -630,7 +630,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -658,7 +658,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -688,7 +688,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -732,7 +732,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -785,7 +785,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -842,7 +842,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -901,7 +901,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -958,7 +958,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1016,7 +1016,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1147,7 +1147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
@@ -1155,10 +1155,10 @@
     {
      "data": {
       "text/plain": [
-       "array([1, 4, 2, 3])"
+       "array([4, 2, 1, 3])"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1171,18 +1171,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 123,
    "metadata": {
     "collapsed": false,
     "deletable": true,
     "editable": true,
-    "scrolled": true
+    "scrolled": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "n_batches 7\n",
+      "\n",
       "Tests Passed\n"
      ]
     }
@@ -1201,6 +1203,8 @@
     "    n_batches = int(len(int_text)/slice_size)\n",
     "    \n",
     "    assert n_batches > 0, 'Maybe your batch size is too big ?'\n",
+    "    \n",
+    "    print('n_batches {}\\n'.format(n_batches))\n",
     "\n",
     "    # input part\n",
     "    _inputs = np.array(int_text[:n_batches*slice_size])\n",
@@ -1209,23 +1213,21 @@
     "    _targets = np.array(int_text[1:n_batches*slice_size + 1])\n",
     "    \n",
     "\n",
-    "    # Go through all inputs, targets and split them into batch_size*seq_len list of items\n",
-    "    # [batch, batch, ...]\n",
-    "    inputs, targets = np.split(_inputs, n_batches), np.split(_targets, n_batches)\n",
-    "    \n",
-    "    # concat inputs and targets\n",
-    "    batches = np.c_[inputs, targets]\n",
+    "    # Group inputs, targets into n_batches x seq_len items \n",
+    "    inputs, targets = np.reshape(_inputs, (-1, n_batches, seq_length)), np.reshape(_targets, (-1, n_batches, seq_length))\n",
     "    \n",
     "    \n",
-    "    # reshape into [2 * inputs]\n",
-    "    batches = batches.reshape((2, batch_size, -1))\n",
-    "    #print(batches)\n",
+    "       \n",
+    "    # Look through the n_batches axes\n",
+    "    #print(inputs[:,0,:])\n",
     "    \n",
+    "    # Swatch the 0 axis with n_batches axes, now 0 axis is n_batches axis\n",
+    "    inputs, targets = np.swapaxes(inputs, 0,1), np.swapaxes(targets, 0,1)\n",
+    "   \n",
     "    \n",
-    "    # Shuffle the batches of sequences without disturbing each sequence order\n",
-    "    # We are using transpose to bring the batch into axes 0 and be able to shuffle it\n",
-    "    np.random.shuffle(np.transpose(batches, (1,2,0)))\n",
-    "    \n",
+    "    # stack inputs and targets on columns\n",
+    "    batches = np.column_stack((inputs, targets))\n",
+    "\n",
     "    \n",
     "    # Reshape into final batches output\n",
     "    batches = batches.reshape((-1, 2, batch_size, seq_length))\n",
@@ -1234,7 +1236,8 @@
     "    return batches\n",
     "\n",
     "\n",
-    "#print(get_batches(np.arange(1,100,1), 10, 5)[0])\n",
+    "#Check first batch result\n",
+    "#print(get_batches(np.arange(1,200,1), 20, 3)[0])\n",
     "\n",
     "\"\"\"\n",
     "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
@@ -1263,7 +1266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 107,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1272,11 +1275,11 @@
    "outputs": [],
    "source": [
     "# Number of Epochs\n",
-    "num_epochs = 1000\n",
+    "num_epochs = 100\n",
     "# Batch Size\n",
-    "batch_size = 128\n",
+    "batch_size = 256\n",
     "# RNN Size\n",
-    "rnn_size = 100\n",
+    "rnn_size = 128\n",
     "# Sequence Length\n",
     "# Use the mean of sentences length as sequence length\n",
     "seq_length = int(s_lengths.mean())\n",
@@ -1304,7 +1307,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 109,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1380,7 +1383,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 110,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1391,116 +1394,249 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch   0 Batch    0/49   train_loss = 8.823\n",
-      "Epoch   0 Batch   10/49   train_loss = 6.394\n",
-      "Epoch   0 Batch   20/49   train_loss = 6.288\n",
-      "Epoch   0 Batch   30/49   train_loss = 6.552\n",
-      "Epoch   0 Batch   40/49   train_loss = 6.792\n",
-      "Epoch   1 Batch    1/49   train_loss = 6.129\n",
-      "Epoch   1 Batch   11/49   train_loss = 6.013\n",
-      "Epoch   1 Batch   21/49   train_loss = 6.131\n",
-      "Epoch   1 Batch   31/49   train_loss = 6.094\n",
-      "Epoch   1 Batch   41/49   train_loss = 6.035\n",
-      "Epoch   2 Batch    2/49   train_loss = 5.961\n",
-      "Epoch   2 Batch   12/49   train_loss = 6.199\n",
-      "Epoch   2 Batch   22/49   train_loss = 6.037\n",
-      "Epoch   2 Batch   32/49   train_loss = 6.311\n",
-      "Epoch   2 Batch   42/49   train_loss = 6.187\n",
-      "Epoch   3 Batch    3/49   train_loss = 6.182\n",
-      "Epoch   3 Batch   13/49   train_loss = 6.023\n",
-      "Epoch   3 Batch   23/49   train_loss = 5.968\n",
-      "Epoch   3 Batch   33/49   train_loss = 5.981\n",
-      "Epoch   3 Batch   43/49   train_loss = 6.098\n",
-      "Epoch   4 Batch    4/49   train_loss = 6.104\n",
-      "Epoch   4 Batch   14/49   train_loss = 5.956\n",
-      "Epoch   4 Batch   24/49   train_loss = 6.170\n",
-      "Epoch   4 Batch   34/49   train_loss = 6.176\n",
-      "Epoch   4 Batch   44/49   train_loss = 6.136\n",
-      "Epoch   5 Batch    5/49   train_loss = 6.161\n",
-      "Epoch   5 Batch   15/49   train_loss = 6.067\n",
-      "Epoch   5 Batch   25/49   train_loss = 6.108\n",
-      "Epoch   5 Batch   35/49   train_loss = 6.007\n",
-      "Epoch   5 Batch   45/49   train_loss = 6.007\n",
-      "Epoch   6 Batch    6/49   train_loss = 5.833\n",
-      "Epoch   6 Batch   16/49   train_loss = 6.090\n",
-      "Epoch   6 Batch   26/49   train_loss = 6.225\n",
-      "Epoch   6 Batch   36/49   train_loss = 6.081\n",
-      "Epoch   6 Batch   46/49   train_loss = 6.062\n",
-      "Epoch   7 Batch    7/49   train_loss = 6.026\n",
-      "Epoch   7 Batch   17/49   train_loss = 5.683\n",
-      "Epoch   7 Batch   27/49   train_loss = 6.055\n",
-      "Epoch   7 Batch   37/49   train_loss = 5.834\n",
-      "Epoch   7 Batch   47/49   train_loss = 6.057\n",
-      "Epoch   8 Batch    8/49   train_loss = 5.782\n",
-      "Epoch   8 Batch   18/49   train_loss = 5.966\n",
-      "Epoch   8 Batch   28/49   train_loss = 6.036\n",
-      "Epoch   8 Batch   38/49   train_loss = 6.007\n",
-      "Epoch   8 Batch   48/49   train_loss = 6.105\n",
-      "Epoch   9 Batch    9/49   train_loss = 5.654\n",
-      "Epoch   9 Batch   19/49   train_loss = 5.723\n",
-      "Epoch   9 Batch   29/49   train_loss = 5.926\n",
-      "Epoch   9 Batch   39/49   train_loss = 5.998\n",
-      "Epoch  10 Batch    0/49   train_loss = 5.719\n",
-      "Epoch  10 Batch   10/49   train_loss = 5.906\n",
-      "Epoch  10 Batch   20/49   train_loss = 5.788\n",
-      "Epoch  10 Batch   30/49   train_loss = 5.764\n",
-      "Epoch  10 Batch   40/49   train_loss = 5.994\n",
-      "Epoch  11 Batch    1/49   train_loss = 5.807\n",
-      "Epoch  11 Batch   11/49   train_loss = 5.796\n",
-      "Epoch  11 Batch   21/49   train_loss = 5.863\n",
-      "Epoch  11 Batch   31/49   train_loss = 5.897\n",
-      "Epoch  11 Batch   41/49   train_loss = 5.779\n",
-      "Epoch  12 Batch    2/49   train_loss = 5.720\n",
-      "Epoch  12 Batch   12/49   train_loss = 5.869\n",
-      "Epoch  12 Batch   22/49   train_loss = 5.764\n",
-      "Epoch  12 Batch   32/49   train_loss = 5.961\n",
-      "Epoch  12 Batch   42/49   train_loss = 5.880\n",
-      "Epoch  13 Batch    3/49   train_loss = 5.819\n",
-      "Epoch  13 Batch   13/49   train_loss = 5.741\n",
-      "Epoch  13 Batch   23/49   train_loss = 5.707\n",
-      "Epoch  13 Batch   33/49   train_loss = 5.683\n",
-      "Epoch  13 Batch   43/49   train_loss = 5.723\n",
-      "Epoch  14 Batch    4/49   train_loss = 5.754\n",
-      "Epoch  14 Batch   14/49   train_loss = 5.721\n",
-      "Epoch  14 Batch   24/49   train_loss = 5.777\n",
-      "Epoch  14 Batch   34/49   train_loss = 5.893\n",
-      "Epoch  14 Batch   44/49   train_loss = 5.771\n",
-      "Epoch  15 Batch    5/49   train_loss = 5.785\n",
-      "Epoch  15 Batch   15/49   train_loss = 5.753\n",
-      "Epoch  15 Batch   25/49   train_loss = 5.703\n",
-      "Epoch  15 Batch   35/49   train_loss = 5.638\n",
-      "Epoch  15 Batch   45/49   train_loss = 5.638\n",
-      "Epoch  16 Batch    6/49   train_loss = 5.463\n",
-      "Epoch  16 Batch   16/49   train_loss = 5.703\n",
-      "Epoch  16 Batch   26/49   train_loss = 5.689\n",
-      "Epoch  16 Batch   36/49   train_loss = 5.740\n",
-      "Epoch  16 Batch   46/49   train_loss = 5.701\n",
-      "Epoch  17 Batch    7/49   train_loss = 5.635\n",
-      "Epoch  17 Batch   17/49   train_loss = 5.377\n",
-      "Epoch  17 Batch   27/49   train_loss = 5.638\n",
-      "Epoch  17 Batch   37/49   train_loss = 5.471\n",
-      "Epoch  17 Batch   47/49   train_loss = 5.639\n",
-      "Epoch  18 Batch    8/49   train_loss = 5.518\n",
-      "Epoch  18 Batch   18/49   train_loss = 5.474\n",
-      "Epoch  18 Batch   28/49   train_loss = 5.561\n",
-      "Epoch  18 Batch   38/49   train_loss = 5.609\n",
-      "Epoch  18 Batch   48/49   train_loss = 5.630\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-74-396c263e7e73>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     16\u001b[0m                 \u001b[0minitial_state\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m                 lr: learning_rate}\n\u001b[0;32m---> 18\u001b[0;31m             \u001b[0mtrain_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal_state\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_op\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m             \u001b[0;31m# Show every <show_every_n_batches> batches\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    765\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    766\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 767\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    768\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    769\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    963\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    964\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 965\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    966\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1013\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1014\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1015\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1016\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1017\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
-      "\u001b[0;32m/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1020\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1021\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1022\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1023\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1024\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1002\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1003\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1004\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1005\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+      "n_batches 24\n",
+      "\n",
+      "Epoch   0 Batch    0/24   train_loss = 8.821\n",
+      "Epoch   0 Batch   10/24   train_loss = 6.639\n",
+      "Epoch   0 Batch   20/24   train_loss = 6.516\n",
+      "Epoch   1 Batch    6/24   train_loss = 6.023\n",
+      "Epoch   1 Batch   16/24   train_loss = 6.070\n",
+      "Epoch   2 Batch    2/24   train_loss = 5.976\n",
+      "Epoch   2 Batch   12/24   train_loss = 6.045\n",
+      "Epoch   2 Batch   22/24   train_loss = 5.987\n",
+      "Epoch   3 Batch    8/24   train_loss = 5.959\n",
+      "Epoch   3 Batch   18/24   train_loss = 5.839\n",
+      "Epoch   4 Batch    4/24   train_loss = 5.702\n",
+      "Epoch   4 Batch   14/24   train_loss = 5.684\n",
+      "Epoch   5 Batch    0/24   train_loss = 5.578\n",
+      "Epoch   5 Batch   10/24   train_loss = 5.687\n",
+      "Epoch   5 Batch   20/24   train_loss = 5.597\n",
+      "Epoch   6 Batch    6/24   train_loss = 5.504\n",
+      "Epoch   6 Batch   16/24   train_loss = 5.442\n",
+      "Epoch   7 Batch    2/24   train_loss = 5.276\n",
+      "Epoch   7 Batch   12/24   train_loss = 5.422\n",
+      "Epoch   7 Batch   22/24   train_loss = 5.317\n",
+      "Epoch   8 Batch    8/24   train_loss = 5.296\n",
+      "Epoch   8 Batch   18/24   train_loss = 5.165\n",
+      "Epoch   9 Batch    4/24   train_loss = 5.019\n",
+      "Epoch   9 Batch   14/24   train_loss = 4.972\n",
+      "Epoch  10 Batch    0/24   train_loss = 4.883\n",
+      "Epoch  10 Batch   10/24   train_loss = 4.991\n",
+      "Epoch  10 Batch   20/24   train_loss = 4.891\n",
+      "Epoch  11 Batch    6/24   train_loss = 4.799\n",
+      "Epoch  11 Batch   16/24   train_loss = 4.764\n",
+      "Epoch  12 Batch    2/24   train_loss = 4.581\n",
+      "Epoch  12 Batch   12/24   train_loss = 4.673\n",
+      "Epoch  12 Batch   22/24   train_loss = 4.586\n",
+      "Epoch  13 Batch    8/24   train_loss = 4.603\n",
+      "Epoch  13 Batch   18/24   train_loss = 4.454\n",
+      "Epoch  14 Batch    4/24   train_loss = 4.280\n",
+      "Epoch  14 Batch   14/24   train_loss = 4.301\n",
+      "Epoch  15 Batch    0/24   train_loss = 4.275\n",
+      "Epoch  15 Batch   10/24   train_loss = 4.371\n",
+      "Epoch  15 Batch   20/24   train_loss = 4.248\n",
+      "Epoch  16 Batch    6/24   train_loss = 4.220\n",
+      "Epoch  16 Batch   16/24   train_loss = 4.169\n",
+      "Epoch  17 Batch    2/24   train_loss = 3.998\n",
+      "Epoch  17 Batch   12/24   train_loss = 4.083\n",
+      "Epoch  17 Batch   22/24   train_loss = 3.965\n",
+      "Epoch  18 Batch    8/24   train_loss = 3.985\n",
+      "Epoch  18 Batch   18/24   train_loss = 3.911\n",
+      "Epoch  19 Batch    4/24   train_loss = 3.733\n",
+      "Epoch  19 Batch   14/24   train_loss = 3.779\n",
+      "Epoch  20 Batch    0/24   train_loss = 3.714\n",
+      "Epoch  20 Batch   10/24   train_loss = 3.793\n",
+      "Epoch  20 Batch   20/24   train_loss = 3.735\n",
+      "Epoch  21 Batch    6/24   train_loss = 3.713\n",
+      "Epoch  21 Batch   16/24   train_loss = 3.677\n",
+      "Epoch  22 Batch    2/24   train_loss = 3.518\n",
+      "Epoch  22 Batch   12/24   train_loss = 3.561\n",
+      "Epoch  22 Batch   22/24   train_loss = 3.501\n",
+      "Epoch  23 Batch    8/24   train_loss = 3.499\n",
+      "Epoch  23 Batch   18/24   train_loss = 3.451\n",
+      "Epoch  24 Batch    4/24   train_loss = 3.291\n",
+      "Epoch  24 Batch   14/24   train_loss = 3.295\n",
+      "Epoch  25 Batch    0/24   train_loss = 3.271\n",
+      "Epoch  25 Batch   10/24   train_loss = 3.331\n",
+      "Epoch  25 Batch   20/24   train_loss = 3.256\n",
+      "Epoch  26 Batch    6/24   train_loss = 3.238\n",
+      "Epoch  26 Batch   16/24   train_loss = 3.167\n",
+      "Epoch  27 Batch    2/24   train_loss = 3.038\n",
+      "Epoch  27 Batch   12/24   train_loss = 3.106\n",
+      "Epoch  27 Batch   22/24   train_loss = 2.985\n",
+      "Epoch  28 Batch    8/24   train_loss = 3.011\n",
+      "Epoch  28 Batch   18/24   train_loss = 2.991\n",
+      "Epoch  29 Batch    4/24   train_loss = 2.907\n",
+      "Epoch  29 Batch   14/24   train_loss = 2.891\n",
+      "Epoch  30 Batch    0/24   train_loss = 2.911\n",
+      "Epoch  30 Batch   10/24   train_loss = 2.948\n",
+      "Epoch  30 Batch   20/24   train_loss = 2.903\n",
+      "Epoch  31 Batch    6/24   train_loss = 2.885\n",
+      "Epoch  31 Batch   16/24   train_loss = 2.808\n",
+      "Epoch  32 Batch    2/24   train_loss = 2.670\n",
+      "Epoch  32 Batch   12/24   train_loss = 2.701\n",
+      "Epoch  32 Batch   22/24   train_loss = 2.591\n",
+      "Epoch  33 Batch    8/24   train_loss = 2.579\n",
+      "Epoch  33 Batch   18/24   train_loss = 2.603\n",
+      "Epoch  34 Batch    4/24   train_loss = 2.498\n",
+      "Epoch  34 Batch   14/24   train_loss = 2.504\n",
+      "Epoch  35 Batch    0/24   train_loss = 2.482\n",
+      "Epoch  35 Batch   10/24   train_loss = 2.530\n",
+      "Epoch  35 Batch   20/24   train_loss = 2.489\n",
+      "Epoch  36 Batch    6/24   train_loss = 2.486\n",
+      "Epoch  36 Batch   16/24   train_loss = 2.417\n",
+      "Epoch  37 Batch    2/24   train_loss = 2.320\n",
+      "Epoch  37 Batch   12/24   train_loss = 2.348\n",
+      "Epoch  37 Batch   22/24   train_loss = 2.287\n",
+      "Epoch  38 Batch    8/24   train_loss = 2.245\n",
+      "Epoch  38 Batch   18/24   train_loss = 2.322\n",
+      "Epoch  39 Batch    4/24   train_loss = 2.230\n",
+      "Epoch  39 Batch   14/24   train_loss = 2.234\n",
+      "Epoch  40 Batch    0/24   train_loss = 2.223\n",
+      "Epoch  40 Batch   10/24   train_loss = 2.263\n",
+      "Epoch  40 Batch   20/24   train_loss = 2.267\n",
+      "Epoch  41 Batch    6/24   train_loss = 2.251\n",
+      "Epoch  41 Batch   16/24   train_loss = 2.179\n",
+      "Epoch  42 Batch    2/24   train_loss = 2.121\n",
+      "Epoch  42 Batch   12/24   train_loss = 2.155\n",
+      "Epoch  42 Batch   22/24   train_loss = 2.100\n",
+      "Epoch  43 Batch    8/24   train_loss = 2.031\n",
+      "Epoch  43 Batch   18/24   train_loss = 2.134\n",
+      "Epoch  44 Batch    4/24   train_loss = 2.016\n",
+      "Epoch  44 Batch   14/24   train_loss = 2.027\n",
+      "Epoch  45 Batch    0/24   train_loss = 2.049\n",
+      "Epoch  45 Batch   10/24   train_loss = 2.042\n",
+      "Epoch  45 Batch   20/24   train_loss = 2.060\n",
+      "Epoch  46 Batch    6/24   train_loss = 2.031\n",
+      "Epoch  46 Batch   16/24   train_loss = 1.943\n",
+      "Epoch  47 Batch    2/24   train_loss = 1.888\n",
+      "Epoch  47 Batch   12/24   train_loss = 1.901\n",
+      "Epoch  47 Batch   22/24   train_loss = 1.881\n",
+      "Epoch  48 Batch    8/24   train_loss = 1.794\n",
+      "Epoch  48 Batch   18/24   train_loss = 1.910\n",
+      "Epoch  49 Batch    4/24   train_loss = 1.802\n",
+      "Epoch  49 Batch   14/24   train_loss = 1.839\n",
+      "Epoch  50 Batch    0/24   train_loss = 1.860\n",
+      "Epoch  50 Batch   10/24   train_loss = 1.836\n",
+      "Epoch  50 Batch   20/24   train_loss = 1.834\n",
+      "Epoch  51 Batch    6/24   train_loss = 1.841\n",
+      "Epoch  51 Batch   16/24   train_loss = 1.803\n",
+      "Epoch  52 Batch    2/24   train_loss = 1.735\n",
+      "Epoch  52 Batch   12/24   train_loss = 1.723\n",
+      "Epoch  52 Batch   22/24   train_loss = 1.692\n",
+      "Epoch  53 Batch    8/24   train_loss = 1.636\n",
+      "Epoch  53 Batch   18/24   train_loss = 1.754\n",
+      "Epoch  54 Batch    4/24   train_loss = 1.671\n",
+      "Epoch  54 Batch   14/24   train_loss = 1.657\n",
+      "Epoch  55 Batch    0/24   train_loss = 1.722\n",
+      "Epoch  55 Batch   10/24   train_loss = 1.697\n",
+      "Epoch  55 Batch   20/24   train_loss = 1.719\n",
+      "Epoch  56 Batch    6/24   train_loss = 1.689\n",
+      "Epoch  56 Batch   16/24   train_loss = 1.624\n",
+      "Epoch  57 Batch    2/24   train_loss = 1.592\n",
+      "Epoch  57 Batch   12/24   train_loss = 1.616\n",
+      "Epoch  57 Batch   22/24   train_loss = 1.556\n",
+      "Epoch  58 Batch    8/24   train_loss = 1.525\n",
+      "Epoch  58 Batch   18/24   train_loss = 1.592\n",
+      "Epoch  59 Batch    4/24   train_loss = 1.547\n",
+      "Epoch  59 Batch   14/24   train_loss = 1.549\n",
+      "Epoch  60 Batch    0/24   train_loss = 1.570\n",
+      "Epoch  60 Batch   10/24   train_loss = 1.530\n",
+      "Epoch  60 Batch   20/24   train_loss = 1.580\n",
+      "Epoch  61 Batch    6/24   train_loss = 1.540\n",
+      "Epoch  61 Batch   16/24   train_loss = 1.496\n",
+      "Epoch  62 Batch    2/24   train_loss = 1.459\n",
+      "Epoch  62 Batch   12/24   train_loss = 1.450\n",
+      "Epoch  62 Batch   22/24   train_loss = 1.432\n",
+      "Epoch  63 Batch    8/24   train_loss = 1.387\n",
+      "Epoch  63 Batch   18/24   train_loss = 1.483\n",
+      "Epoch  64 Batch    4/24   train_loss = 1.414\n",
+      "Epoch  64 Batch   14/24   train_loss = 1.430\n",
+      "Epoch  65 Batch    0/24   train_loss = 1.452\n",
+      "Epoch  65 Batch   10/24   train_loss = 1.433\n",
+      "Epoch  65 Batch   20/24   train_loss = 1.496\n",
+      "Epoch  66 Batch    6/24   train_loss = 1.447\n",
+      "Epoch  66 Batch   16/24   train_loss = 1.436\n",
+      "Epoch  67 Batch    2/24   train_loss = 1.398\n",
+      "Epoch  67 Batch   12/24   train_loss = 1.409\n",
+      "Epoch  67 Batch   22/24   train_loss = 1.404\n",
+      "Epoch  68 Batch    8/24   train_loss = 1.339\n",
+      "Epoch  68 Batch   18/24   train_loss = 1.429\n",
+      "Epoch  69 Batch    4/24   train_loss = 1.371\n",
+      "Epoch  69 Batch   14/24   train_loss = 1.402\n",
+      "Epoch  70 Batch    0/24   train_loss = 1.439\n",
+      "Epoch  70 Batch   10/24   train_loss = 1.382\n",
+      "Epoch  70 Batch   20/24   train_loss = 1.436\n",
+      "Epoch  71 Batch    6/24   train_loss = 1.411\n",
+      "Epoch  71 Batch   16/24   train_loss = 1.379\n",
+      "Epoch  72 Batch    2/24   train_loss = 1.347\n",
+      "Epoch  72 Batch   12/24   train_loss = 1.326\n",
+      "Epoch  72 Batch   22/24   train_loss = 1.335\n",
+      "Epoch  73 Batch    8/24   train_loss = 1.284\n",
+      "Epoch  73 Batch   18/24   train_loss = 1.369\n",
+      "Epoch  74 Batch    4/24   train_loss = 1.308\n",
+      "Epoch  74 Batch   14/24   train_loss = 1.305\n",
+      "Epoch  75 Batch    0/24   train_loss = 1.329\n",
+      "Epoch  75 Batch   10/24   train_loss = 1.291\n",
+      "Epoch  75 Batch   20/24   train_loss = 1.345\n",
+      "Epoch  76 Batch    6/24   train_loss = 1.303\n",
+      "Epoch  76 Batch   16/24   train_loss = 1.287\n",
+      "Epoch  77 Batch    2/24   train_loss = 1.260\n",
+      "Epoch  77 Batch   12/24   train_loss = 1.229\n",
+      "Epoch  77 Batch   22/24   train_loss = 1.228\n",
+      "Epoch  78 Batch    8/24   train_loss = 1.176\n",
+      "Epoch  78 Batch   18/24   train_loss = 1.247\n",
+      "Epoch  79 Batch    4/24   train_loss = 1.176\n",
+      "Epoch  79 Batch   14/24   train_loss = 1.211\n",
+      "Epoch  80 Batch    0/24   train_loss = 1.255\n",
+      "Epoch  80 Batch   10/24   train_loss = 1.214\n",
+      "Epoch  80 Batch   20/24   train_loss = 1.233\n",
+      "Epoch  81 Batch    6/24   train_loss = 1.194\n",
+      "Epoch  81 Batch   16/24   train_loss = 1.173\n",
+      "Epoch  82 Batch    2/24   train_loss = 1.179\n",
+      "Epoch  82 Batch   12/24   train_loss = 1.115\n",
+      "Epoch  82 Batch   22/24   train_loss = 1.123\n",
+      "Epoch  83 Batch    8/24   train_loss = 1.058\n",
+      "Epoch  83 Batch   18/24   train_loss = 1.138\n",
+      "Epoch  84 Batch    4/24   train_loss = 1.085\n",
+      "Epoch  84 Batch   14/24   train_loss = 1.120\n",
+      "Epoch  85 Batch    0/24   train_loss = 1.157\n",
+      "Epoch  85 Batch   10/24   train_loss = 1.081\n",
+      "Epoch  85 Batch   20/24   train_loss = 1.157\n",
+      "Epoch  86 Batch    6/24   train_loss = 1.129\n",
+      "Epoch  86 Batch   16/24   train_loss = 1.081\n",
+      "Epoch  87 Batch    2/24   train_loss = 1.078\n",
+      "Epoch  87 Batch   12/24   train_loss = 1.033\n",
+      "Epoch  87 Batch   22/24   train_loss = 1.048\n",
+      "Epoch  88 Batch    8/24   train_loss = 1.021\n",
+      "Epoch  88 Batch   18/24   train_loss = 1.088\n",
+      "Epoch  89 Batch    4/24   train_loss = 1.019\n",
+      "Epoch  89 Batch   14/24   train_loss = 1.046\n",
+      "Epoch  90 Batch    0/24   train_loss = 1.085\n",
+      "Epoch  90 Batch   10/24   train_loss = 1.045\n",
+      "Epoch  90 Batch   20/24   train_loss = 1.093\n",
+      "Epoch  91 Batch    6/24   train_loss = 1.056\n",
+      "Epoch  91 Batch   16/24   train_loss = 1.035\n",
+      "Epoch  92 Batch    2/24   train_loss = 1.047\n",
+      "Epoch  92 Batch   12/24   train_loss = 1.002\n",
+      "Epoch  92 Batch   22/24   train_loss = 1.011\n",
+      "Epoch  93 Batch    8/24   train_loss = 0.952\n",
+      "Epoch  93 Batch   18/24   train_loss = 1.036\n",
+      "Epoch  94 Batch    4/24   train_loss = 0.982\n",
+      "Epoch  94 Batch   14/24   train_loss = 1.020\n",
+      "Epoch  95 Batch    0/24   train_loss = 1.029\n",
+      "Epoch  95 Batch   10/24   train_loss = 0.961\n",
+      "Epoch  95 Batch   20/24   train_loss = 0.999\n",
+      "Epoch  96 Batch    6/24   train_loss = 0.982\n",
+      "Epoch  96 Batch   16/24   train_loss = 0.979\n",
+      "Epoch  97 Batch    2/24   train_loss = 0.980\n",
+      "Epoch  97 Batch   12/24   train_loss = 0.938\n",
+      "Epoch  97 Batch   22/24   train_loss = 0.939\n",
+      "Epoch  98 Batch    8/24   train_loss = 0.902\n",
+      "Epoch  98 Batch   18/24   train_loss = 0.976\n",
+      "Epoch  99 Batch    4/24   train_loss = 0.920\n",
+      "Epoch  99 Batch   14/24   train_loss = 0.949\n",
+      "Model Trained and Saved\n"
      ]
     }
    ],
@@ -1551,7 +1687,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 198,
+   "execution_count": 111,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1578,7 +1714,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 272,
+   "execution_count": 112,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1618,7 +1754,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 273,
+   "execution_count": 113,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1654,62 +1790,6 @@
     "tests.test_get_tensors(get_tensors)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 511,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true,
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(3,)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "3"
-      ]
-     },
-     "execution_count": 511,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t = np.array([1,2,3])\n",
-    "print(t.shape)\n",
-    "np.extract(t==3, t)[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 458,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 458,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -1723,7 +1803,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 472,
+   "execution_count": 121,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1734,20 +1814,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.8\n"
-     ]
-    },
-    {
-     "ename": "IndexError",
-     "evalue": "only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-472-ec56654dfaa2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0mDON\u001b[0m\u001b[0;31m'\u001b[0m\u001b[0mT\u001b[0m \u001b[0mMODIFY\u001b[0m \u001b[0mANYTHING\u001b[0m \u001b[0mIN\u001b[0m \u001b[0mTHIS\u001b[0m \u001b[0mCELL\u001b[0m \u001b[0mTHAT\u001b[0m \u001b[0mIS\u001b[0m \u001b[0mBELOW\u001b[0m \u001b[0mTHIS\u001b[0m \u001b[0mLINE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \"\"\"\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mtests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest_pick_word\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpick_word\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m/home/spike/ml/udacity/nd101/deep-learning-modified/tv-script-generation/problem_unittests.py\u001b[0m in \u001b[0;36mtest_pick_word\u001b[0;34m(pick_word)\u001b[0m\n\u001b[1;32m    283\u001b[0m         \u001b[0mtest_int_to_vocab\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mword_i\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mword\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mword_i\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mword\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'this'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'is'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'a'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'test'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    284\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 285\u001b[0;31m         \u001b[0mpred_word\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpick_word\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_probabilities\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_int_to_vocab\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    287\u001b[0m         \u001b[0;31m# Check type\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-472-ec56654dfaa2>\u001b[0m in \u001b[0;36mpick_word\u001b[0;34m(probabilities, int_to_vocab)\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprobabilities\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprobabilities\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mint_to_vocab\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mprobabilities\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprobabilities\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprobabilities\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mIndexError\u001b[0m: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices"
+      "Tests Passed\n"
      ]
     }
    ],
@@ -1761,9 +1828,10 @@
     "    \"\"\"\n",
     "    \n",
     "    word_p = np.random.choice(probabilities,p=probabilities)\n",
-    "    word = np.extract(probabilities==word_p, probabilities)\n",
+    "    word = probabilities.tolist().index(word_p)\n",
+    "    \n",
     "    \n",
-    "    return int_to_vocab[probabilities[np.random.choice(probabilities,p=probabilities)]]\n",
+    "    return int_to_vocab[word]\n",
     "\n",
     "\n",
     "\"\"\"\n",
@@ -1785,7 +1853,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 275,
+   "execution_count": 122,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1796,7 +1864,28 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "moe_szyslak: sizes good-looking slap detective_homer_simpson: takin' cesss planning parrot smoke parrot sizes frustrated choked slap gesture elmo's jerry duff's butterball officials sizes themselves gesture whiny irrelevant paintings continuing huddle tony butterball worst jerry neighborhood slap slap slap detective_homer_simpson: meatpies crooks sail slap slap slap sizes worst mr slap worst gesture parrot calendars bathed schnapps butterball stuck jerry dash my-y-y-y-y-y slap slap slap detective_homer_simpson: rain gesture bashir's jerry longest slap slap slap detective_homer_simpson: realize gesture parrot neighborhood jerry dad's poet presided scrutinizes presided rope neighborhood booth detective_homer_simpson: enjoyed gesture electronic sam: jerry dash my-y-y-y-y-y butterball protestantism dash my-y-y-y-y-y friendly dash happiness agreement slap protestantism muttering muttering sugar-free parrot is: abandon fudd scrutinizes detective_homer_simpson: itself duff's butterball drinker slap muttering shaky slap cuff giant face knockin' tv-station_announcer: that's slap detective_homer_simpson: celebrate rubbed 2nd_voice_on_transmitter: further rubbed usual laramie bunch slap detective_homer_simpson: itself gesture child jerry premise poet sarcastic slap detective_homer_simpson: meatpies skydiving scrutinizes scream renee: scrutinizes detective_homer_simpson: itself lenses butterball tapered smokin' 2nd_voice_on_transmitter: slap detective_homer_simpson: detective_homer_simpson: detective_homer_simpson: aims always butterball oh-so-sophisticated wine dislike sizes bury gang butterball renee: rope laramie themselves beings slap detective_homer_simpson: rain indicates butterball stunned slap detective_homer_simpson: rain arts butterball ratted 2nd_voice_on_transmitter: pepsi oh-so-sophisticated planning booth rope presided rope abandon worst\n"
+      "moe_szyslak: okay, you can't sell it till it blows at the mr.\n",
+      "moe_szyslak: what?\n",
+      "homer_simpson: do you goin' homer?\n",
+      "moe_szyslak: oh, why don't you had? he's just a day?\n",
+      "homer_simpson: marge if it isn't little call? i'm gonna invite me out.\n",
+      "barney_gumble:(to homer) all knows me? hey, but all day it's everyone anymore.\n",
+      "moe_szyslak: aw, behind not one about us, like how about the last guy old.\n",
+      "lenny_leonard: hey, what that can hide!\n",
+      "seymour_skinner: i had to my time you say, big day...\n",
+      "kirk_van_houten: oh, nuts.\n",
+      "homer_simpson: when i'm not your best friend because i'm gonna plotz.\n",
+      "homer_simpson: don't be lost in a grampa.\n",
+      "homer_simpson: oh, right. it wasn't up from the\" sex on the car.\n",
+      "moe_szyslak: ah, he's down for a man. to be a friend.\n",
+      "\n",
+      "\n",
+      "homer_simpson:(moans) we're sure is.\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "homer_simpson:(to tv) hey, hibachi head.\n"
      ]
     }
    ],
diff --git a/tv-script-generation/logs/1/events.out.tfevents.1490999151.ip-172-31-18-64 b/tv-script-generation/logs/1/events.out.tfevents.1490999151.ip-172-31-18-64
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diff --git a/tv-script-generation/params.p b/tv-script-generation/params.p
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+++ b/tv-script-generation/params.p
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diff --git a/tv-script-generation/preprocess.p b/tv-script-generation/preprocess.p
index 724c7e3..c8f517d 100644
--- a/tv-script-generation/preprocess.p
+++ b/tv-script-generation/preprocess.p
@@ -1,3 +1,3 @@
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+size 386694
diff --git a/tv-script-generation/save.data-00000-of-00001 b/tv-script-generation/save.data-00000-of-00001
index de861ec..452f6b9 100644
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+++ b/tv-script-generation/save.data-00000-of-00001
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+size 20692700
diff --git a/tv-script-generation/save.index b/tv-script-generation/save.index
index 3a25b31..e93fbdb 100644
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+++ b/tv-script-generation/save.index
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diff --git a/tv-script-generation/save.meta b/tv-script-generation/save.meta
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